{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Learning Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function to plot learning curves for classifiers. Learning curves are extremely useful to analyze if a model is suffering from over- or under-fitting (high variance or high bias). The function can be imported via\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.plotting import plot_learning_curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dhw6VuCVG6s1jKiMjQ2GeAQMGMGbMGIXj5dixY/Tt25dly5axcuXKUm33dSXZ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uKLLzodW0Uy7Nvm8Nja4Q7UOg1LPL59gJHmPgaOp+SVH1lU+PJ0FqNaeZZrnOVOUch98DFMH7xq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\nDt2xhKiqJMkXTmvTpg0HDx4E8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8JfcAzyVvdfeumlMotdiIoyMMSj2ib1xRlyswe5VpWJ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\nQohO5v8BjmuAzuIbFvQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb8a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import plot_learning_curves\n",
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.data import iris_data\n",
    "from mlxtend.preprocessing import shuffle_arrays_unison\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Loading some example data\n",
    "X, y = iris_data()\n",
    "X, y = shuffle_arrays_unison(arrays=[X, y], random_seed=123)\n",
    "X_train, X_test = X[:100], X[100:]\n",
    "y_train, y_test = y[:100], y[100:]\n",
    "\n",
    "clf = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "plot_learning_curves(X_train, y_train, X_test, y_test, clf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## plot_learning_curves\n",
      "\n",
      "*plot_learning_curves(X_train, y_train, X_test, y_test, clf, train_marker='o', test_marker='^', scoring='misclassification error', suppress_plot=False, print_model=True, style='fivethirtyeight', legend_loc='best')*\n",
      "\n",
      "Plots learning curves of a classifier.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X_train` : array-like, shape = [n_samples, n_features]\n",
      "\n",
      "    Feature matrix of the training dataset.\n",
      "\n",
      "- `y_train` : array-like, shape = [n_samples]\n",
      "\n",
      "    True class labels of the training dataset.\n",
      "\n",
      "- `X_test` : array-like, shape = [n_samples, n_features]\n",
      "\n",
      "    Feature matrix of the test dataset.\n",
      "\n",
      "- `y_test` : array-like, shape = [n_samples]\n",
      "\n",
      "    True class labels of the test dataset.\n",
      "\n",
      "- `clf` : Classifier object. Must have a .predict .fit method.\n",
      "\n",
      "\n",
      "- `train_marker` : str (default: 'o')\n",
      "\n",
      "    Marker for the training set line plot.\n",
      "\n",
      "- `test_marker` : str (default: '^')\n",
      "\n",
      "    Marker for the test set line plot.\n",
      "\n",
      "- `scoring` : str (default: 'misclassification error')\n",
      "\n",
      "    If not 'misclassification error', accepts the following metrics\n",
      "    (from scikit-learn):\n",
      "    {'accuracy', 'average_precision', 'f1_micro', 'f1_macro',\n",
      "    'f1_weighted', 'f1_samples', 'log_loss',\n",
      "    'precision', 'recall', 'roc_auc',\n",
      "    'adjusted_rand_score', 'mean_absolute_error', 'mean_squared_error',\n",
      "    'median_absolute_error', 'r2'}\n",
      "\n",
      "- `suppress_plot=False` : bool (default: False)\n",
      "\n",
      "    Suppress matplotlib plots if True. Recommended\n",
      "    for testing purposes.\n",
      "\n",
      "- `print_model` : bool (default: True)\n",
      "\n",
      "    Print model parameters in plot title if True.\n",
      "\n",
      "- `style` : str (default: 'fivethirtyeight')\n",
      "\n",
      "    Matplotlib style\n",
      "\n",
      "- `legend_loc` : str (default: 'best')\n",
      "\n",
      "    Where to place the plot legend:\n",
      "    {'best', 'upper left', 'upper right', 'lower left', 'lower right'}\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `errors` : (training_error, test_error): tuple of lists\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/plot_learning_curves.md', 'r') as f:\n",
    "    print(f.read())"
   ]
  }
 ],
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